Abstract
Applications and Challenges of Artificial Intelligence in Space Missions
Highlights
Artificial Intelligence (AI) has witnessed a growing interest in the space community over the last two decades
In view of the above challenges there seems to be a push towards finding ways to collaboratively improve Machine Learning (ML) models hosted on public blockchains
The application of ML algorithms to various aspects of remote sensing, spacecraft health monitoring and communication offers the potential to improve throughput and data return to Earth from space missions
Summary
AI has witnessed a growing interest in the space community over the last two decades. While multiple surveys on the applications of AI in space missions have been published, many of them are old and do not cover some crucial and recent developments in the field; such as the contributions of Deep Learning (DL) and bioinspired AI algorithms. We identified nine recently developed bioinspired optimization algorithms and their potential application areas in space missions. New Bioinspired Algorithms (BIAs) have been developed to help overcome the limitations of traditional AI algorithms, especially when it concerns the optimization of multiobjective problems [53] These algorithms naturally tend to have a higher efficiency than the traditional AI methods; with the two most widely accepted categories being evolutionary and swarm algorithms [54, 55]. Given that its range of behaviors is solely limited to the moves of a chess game, it cannot be said to meet all the requirements for agency
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